WU YUANYUAN

Lingnan University, Hong Kang, CN

Renaissance 2025, 1(02); https://doi.org/10.70548/ra142142
Submission received: 8 January 2025 / Revised: 27 January 2025 / Accepted: 23 February 2025 / Published: 17 March 2025

Abstract

This study explores the fairness of China’s smart elderly care policies in the context of the digital divide, examining its effect on policy implementation and equity. Using a quantitative approach, we surveyed 800 elderly individuals across four cities in Guangdong Province. By integrating the TAM and IDT, the study analyzes technology adoption and its impact on service usage and perceived fairness. Findings reveal that the digital divide worsens inequalities, with regional differences and technological literacy as key factors. Elderly in less developed regions and with lower tech skills face greater challenges in accessing services. Smart elderly care has a dual nature: technology boosts efficiency but widens gaps. We suggest improving infrastructure, offering tech training, and designing user-friendly devices to reduce the divide and enhance fairness.

KEYWORDS：Digital Divide, Smart Elderly Care, TAM, IDT, Policy Fairness

1. Introduction  

China’s rapidly aging population presents a significant challenge to the nation’s social and economic development, straining existing healthcare and social welfare systems. As of recent estimates, the number of individuals aged 60 and above in China has surpassed 280 million, constituting approximately 20% of the total population, a proportion that continues to grow at an unprecedented rate (Luo & Su, 2021). This demographic shift is accompanied by a rising number of older adults living alone or grappling with disabilities, which further exacerbates the demand for accessible and efficient elderly care services. In response to these challenges, smart elderly care has emerged as a promising model, leveraging advanced technologies such as wearable health monitoring devices, telemedicine platforms, and artificial intelligence to enhance the quality and efficiency of care services for the elderly (Hong, 2022). For instance, wearable devices enable real-time tracking of vital signs, while telemedicine facilitates remote consultations, offering critical support to elderly individuals who may otherwise lack access to timely medical care (Wu et al., 2020). The Chinese government has played a proactive role in promoting the development of smart elderly care, implementing a range of supportive measures including policy frameworks, financial subsidies, and the establishment of industry standards to foster innovation and adoption (Qian, 2024). A notable example is the 14th Five-Year Plan for National Aging Undertakings and Elderly Care Service System (2022), which explicitly calls for the accelerated application of smart elderly care technologies and encourages collaboration among government, private sectors, and social organizations to drive industrial integration (State Council of the People’s Republic of China, 2022).

Despite these advancements, the digital divide remains a formidable obstacle to the equitable implementation of smart elderly care policies. Many elderly individuals, particularly those in economically disadvantaged or rural areas, face significant barriers to accessing these technologies. For instance, a considerable number of seniors cannot afford smart devices due to limited financial resources, while others in remote regions lack reliable network infrastructure, such as broadband or 5G connectivity, to support digital services (Sun, 2024). Additionally, a lack of digital literacy poses a substantial challenge, as many elderly individuals struggle to operate smart devices or comprehend complex digital information, further limiting their ability to benefit from technology-driven care solutions (Zhang et al., 2020). These challenges are particularly pronounced for elderly populations in remote areas or those with low digital literacy, where the absence of technological access and skills can deepen existing inequalities in elderly care services (Chen & Chan, 2022). Consequently, the digital divide risks marginalizing vulnerable groups, exacerbating disparities in access to care and undermining the fairness of smart elderly care policies.

This study aims to investigate the impact of the digital divide on the fairness of smart elderly care policies in China, with a focus on identifying and addressing inequalities in policy implementation. By examining the interplay between technology access, digital literacy, and care equity, the research seeks to propose actionable solutions to bridge the digital gap and ensure more inclusive elderly care services. Specifically, the study addresses three key research questions: (1) Do smart elderly care policies disproportionately exacerbate inequalities for specific elderly groups, such as those in remote areas or with limited digital literacy? (2) To what extent is the digital divide the primary driver of inequality in access to care services? (3) How can policy optimization and technological innovation be leveraged to narrow the digital divide and promote more equitable elderly care services? To answer these questions, this research employs the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) as a combined theoretical framework to explore how older adults interact with smart elderly care technologies and how these interactions influence fairness in service access. TAM provides insights into individual-level factors, such as perceived ease of use and usefulness, that affect technology adoption, while IDT examines societal factors, such as regional infrastructure and social norms, that shape the diffusion of innovations (Davis, 1989; Rogers, 1995). Through quantitative data analysis, including surveys and statistical modeling, the study also evaluates the role of the digital divide in care equity, offering evidence-based recommendations for policymakers to enhance the inclusivity and sustainability of smart elderly care systems in China.

2. Literature Review

2.1 Concept and Development of Smart Elderly Care

Smart elderly care refers to a care model that utilizes information technology, the Internet of Things (IoT), and artificial intelligence to provide services such as health monitoring, telemedicine, and emergency assistance for the elderly (Hung, 2022). In recent years, as China’s population aging has intensified, smart elderly care has been regarded as a crucial solution to address the shortage of traditional elderly care resources. The government has actively promoted the development of the smart elderly care industry through policy guidance, financial subsidies, and the establishment of industry standards. For example, the “14th Five-Year Plan for the Development of National Aging Undertakings and the Elderly Care Service System” released in 2022 explicitly proposed accelerating the application of smart elderly care technologies and encouraging multi-stakeholder participation and industrial integration (Qian, 2024). The application of smart elderly care has significantly improved the quality of life for older adults. For instance, wearable devices enable real-time health monitoring, and telemedicine platforms provide diagnostic and treatment services for elderly individuals in remote areas (Wu et al., 2020).

However, the widespread adoption of smart elderly care still faces numerous challenges, particularly in terms of technology application and data security (Lee et al., 2021). Existing studies have primarily focused on the technological development and policy support for smart elderly care, but less attention has been paid to issues of fairness in policy implementation.

2.2 Digital Divide and Equity in Elderly Care

The digital divide refers to the unequal access to and use of information and communication technologies (ICTs) among different social groups, a disparity that has grown with rapid technological advancement. It primarily manifests in three dimensions: hardware access, network coverage, and digital literacy (Zhang et al., 2020). Hardware access involves the ability to acquire devices like smartphones or wearables, network coverage pertains to reliable internet availability, and digital literacy encompasses the skills needed to navigate digital technologies. In the context of smart elderly care, the digital divide significantly impacts older adults, limiting their ability to benefit from technology-driven services. Many elderly individuals cannot afford smart devices due to financial constraints, while those in remote areas often lack adequate network infrastructure, such as broadband or 5G connectivity, to support digital care services (Sun, 2024). For example, rural regions in China frequently face unstable internet access, excluding elderly residents from services like telemedicine or remote health monitoring (Liu & Wang, 2022).

Additionally, low digital literacy poses a major barrier for older adults in adopting smart elderly care technologies. Many lack the technical skills to operate devices, such as navigating apps or troubleshooting issues, and struggle to understand complex digital information, like health data on online platforms (Liu & Wang, 2022). This challenge is particularly pronounced for those with limited education or minimal prior exposure to technology, making it difficult for them to engage with smart elderly care services. Studies reveal a significant urban-rural divide in service usage, with urban elderly in cities like Shanghai benefiting from better access to high-speed internet and devices, while rural elderly in provinces like Yunnan lag behind, exacerbating care inequalities (Xu et al., 2023). The urban-rural digital divide thus deepens disparities in access to technology-driven care solutions.

Moreover, elderly individuals with low digital literacy often exhibit hesitation toward smart devices, driven by concerns over data privacy and security risks. Many fear that their personal health information could be misused, which further discourages adoption (Lee et al., 2021). While some studies have explored how the digital divide restricts access to elderly care services, there remains a gap in understanding its impact on the fairness of policy implementation (Zhao & Liu, 2022). Few analyses have examined how disparities in technology access and digital skills affect the equitable distribution of smart elderly care services, particularly in policy contexts. This study addresses this gap by investigating the role of the digital divide in perpetuating inequities in smart elderly care, focusing on its implications for policy fairness and inclusivity across diverse elderly populations in China.

2.3 Technology Acceptance and Innovation Diffusion Theories

This study combines the Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT) to explore how elderly individuals adopt technology in smart elderly care. TAM, developed by Davis in 1989, highlights that people are more likely to use technology if they find it useful and easy to use. Research shows that older adults’ acceptance of smart elderly care services depends a lot on how simple the devices are and how practical their features feel (Sun, 2024). But TAM mainly looks at individual behavior and doesn’t consider broader social factors that affect technology use.

On the other hand, IDT, introduced by Rogers in 1995, focuses on how new technologies spread through social systems. It points out that factors like the benefits, compatibility, and complexity of a technology influence how quickly it spreads. IDT has been widely used to study smart elderly care services. For example, Wu (2020) found that these services spread better when they fit into older adults’ daily routines and aren’t too complicated to use.

By blending TAM and IDT, this study looks at both individual and social angles: TAM helps us understand how elderly users accept technology, while IDT shows how social systems—like policy support or regional differences—shape technology spread. Few studies have paired these theories in smart elderly care, so this research aims to bridge that gap by drawing on their combined strengths.

2.4 Limitations of Existing Research

Research on smart elderly care and the digital divide has made some progress, but there are still a few gaps. First, most studies focus on tech development and policy-making. They don’t pay much attention to fairness issues caused by the digital divide during policy rollout (Liu et al., 2021). Second, there’s not enough research on regional differences in China. For example, we need more studies on how urban-rural gaps and economic differences affect access to smart elderly care services. Lastly, many studies lack a solid theoretical framework. They don’t fully explore how the digital divide impacts care equity from both individual and societal angles.

This study combines the TAM and IDT with data on regional disparities in China. It aims to reveal how the digital divide affects the fairness of smart elderly care policies. Plus, it hopes to offer both theoretical insights and practical ideas for improving policies.

3. Methodology

3.1 Designing a Study on Smart Elderly Care

This study adopts a quantitative research approach, utilizing surveys and statistical data analysis, to investigate the impact of the digital divide on the fairness of smart elderly care policies in China. The quantitative methodology is particularly effective for handling large datasets, enabling the systematic analysis of patterns and relationships across diverse groups of elderly individuals (Creswell, 2014). By collecting numerical data through structured surveys, this study tests hypotheses to establish a clear connection between the digital divide and equity in elderly care services, focusing on how disparities in technology access and usage influence policy outcomes. Specifically, the research examines variables such as regional economic differences, digital literacy levels, and income disparities to uncover the extent to which these factors contribute to unequal access to smart elderly care services.

To provide a comprehensive understanding of technology adoption among the elderly, this study integrates the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) to develop a hybrid theoretical framework. TAM, which emphasizes perceived usefulness and ease of use as key determinants of technology acceptance, helps analyze individual-level factors affecting elderly users’ willingness to adopt smart elderly care services (Davis, 1989). Meanwhile, IDT, which focuses on the societal diffusion of innovations, sheds light on broader systemic factors—such as regional infrastructure and social norms—that influence the spread of these technologies across different communities (Rogers, 1995). By combining these two perspectives, the hybrid model examines elderly individuals’ adoption behavior from both individual (technology acceptance) and societal (technology diffusion) angles, offering a multidimensional view of how the digital divide shapes the fairness of smart elderly care policies. This integrated approach not only enhances the theoretical depth of the study but also provides a robust framework for understanding the complex interplay between technology adoption and policy equity, contributing to both academic research and practical policy design in the field of smart elderly care.

3.2 Research Subjects

The study population consisted of elderly individuals aged 60 and above in four cities in Guangdong Province, including Shenzhen and Guangzhou (economically developed regions) and Meizhou and Heyuan (economically underdeveloped regions). A random sample of 200 participants was selected from each city, resulting in a total sample size of 800 individuals. These regions were chosen to reflect the imbalance in regional economic development in China, where developed regions have better network infrastructure, while underdeveloped regions face greater digital divides .

A stratified random sampling method was used. First, cities were stratified, and then participants were randomly selected through community service centers and elderly care institutions to ensure sample representativeness. 

3.3 Collecting Data on Digital Divide Effects

We gathered information through a survey tailored to smart elderly care, drawing on prior research (e.g., Sun, 2024). The survey covered three areas: (1) personal details like age, gender, and income; (2) how often participants used smart elderly care services, their sense of fairness, and their satisfaction levels, rated on a five-point scale; and (3) their tech skills and overall health.

The variables included dependent variables (service usage, perceived fairness, satisfaction) and independent variables (regional disparities, income level, education level, health status), with digital literacy as a mediating variable.

3.4 Analyzing Data for Policy Fairness

Data analysis was conducted using SPSSAU with the following methods:

Descriptive Statistics: Analyzed sample characteristics and the means and standard deviations of variables to understand the basic situation of elderly individuals in different regions.

Regression Analysis:Used the model Y=β₀+β₁X₁+β₂X₂+⋯+β_nX_n+ϵ to examine the impact of the digital divide (e.g., regional disparities, digital literacy) on service usage, perceived fairness, and service satisfaction, testing Hypothesis 1 and Hypothesis 2.

Difference Analysis:Conducted T-tests and ANOVA to compare differences among groups with varying regional backgrounds, digital literacy levels, and income levels.

To ensure the robustness of the analysis, the data underwent normality testing (Kolmogorov-Smirnov test) and multicollinearity testing (VIF values). The results indicated that the data met the requirements for analysis .

4.Results and Discussion

4.1 Presenting Data on Service Usage Gaps

4.1.1 Description of Sample Characteristics

A total of 800 valid questionnaires were collected in this study. The average age of the participants was 68.3 years (SD=5.2), with 42.5% (340 participants) being male and 57.5% (460 participants) being female. By region, there were 200 participants from Shenzhen and Guangzhou (developed regions) and 200 participants from Meizhou and Heyuan (underdeveloped regions). The distributions of income levels, education levels, and health statuses also reflect the diversity of the sample. The average score for digital literacy was 2.1 (SD=0.9, on a 1–5 scale), indicating that the overall technological operation ability of the sample was relatively low.

4.1.2 Use of Smart Elderly Care Services

The Overall mean score for the use of smart elderly care services was 2.3 (SD=1.1), indicating a relatively low frequency of use among elderly individuals. This may be due to the complexity of the technology or limited access to services. Table 1 shows that the use score in developed regions (M=2.8, SD=1.0) was significantly higher than that in underdeveloped regions (M=1.8, SD=0.9, t=12.45, p<0.001). This gap may result from better network infrastructure and higher equipment penetration rates in developed regions (e.g., Shenzhen), while insufficient infrastructure in underdeveloped regions (e.g., Meizhou) limits service usage.

When grouped by digital literacy, the low digital literacy group (M=1.7, SD=0.8) scored significantly lower than the high digital literacy group (M=3.1, SD=1.0, t=18.32, p<0.001), reflecting the direct impact of technological operation ability on service adoption. Elderly individuals with low digital literacy may abandon the use of services due to operational difficulties.

The effect of income level was examined through ANOVA, which showed a significant difference (F=25.67, p<0.001). The high-income group (M=3.0, SD=1.1) scored higher than the low-income group (M=1.9, SD=0.9), indicating that economic capacity determines the willingness and ability to purchase and use devices.

4.1.3 Perceived Fairness and Service Satisfaction

The overall mean score for perceived fairness was 2.5 (SD=1.0), and the mean score for service satisfaction was 2.7 (SD=1.1), both of which were relatively low. This reflects that elderly individuals generally hold less favorable evaluations of the fairness and satisfaction of smart elderly care services.

Table 1 shows that the perceived fairness (M=3.0, SD=0.9) and satisfaction (M=3.2, SD=1.0) of elderly individuals in developed regions were significantly higher than those in underdeveloped regions (perceived fairness M=2.0, SD=0.8, t=14.78, p<0.001; satisfaction M=2.2, SD=0.9, t=13.22, p<0.001). This disparity may be due to the lack of service resources in underdeveloped regions, leaving elderly individuals feeling marginalized.

The low digital literacy group had lower scores for perceived fairness (M=2.1, SD=0.7) and satisfaction (M=2.3, SD=0.8) compared to the high digital literacy group (perceived fairness M=3.0, SD=0.9, t=16.54, p<0.001; satisfaction M=3.3, SD=1.0, t=15.89, p<0.001). This indicates that elderly individuals with lower technical abilities may feel unfairly treated and experience lower satisfaction due to their inability to effectively use the services.

Table 1: Group Comparisons of Service Usage, Perceived Fairness, and Service Satisfaction

4.1.4 Regression Analysis

The regression analysis used the model Y=β₀+β₁X₁+β₂X₂+⋯+β_nX_n+ϵ t. The results are shown in Table 2.

Service Usage as the Dependent Variable:​​Regional disparities (β=-0.42, p<0.001) indicated that elderly individuals in underdeveloped regions used services significantly less than those in developed regions, which may be due to differences in infrastructure. Digital literacy (β=0.35, p<0.001) showed that for every one-unit increase in technical ability, the service usage score increased by 0.35, highlighting the importance of technology training. Income level (β=0.28, p<0.01) demonstrated a positive impact of economic capacity on service adoption (R²=0.41).

​Perceived Fairness as the Dependent Variable:​​Regional disparities (β=-0.38,p<0.001) and digital literacy (β=0.32, p<0.001) were also significant. Education level (β=0.25, p<0.01) indicated that elderly individuals with higher education levels perceived greater fairness (R²=0.37).

Service Satisfaction as the Dependent Variable:​​Digital literacy (β=0.40, p<0.001) had the greatest influence, followed by regional disparities (β=-0.35, p<0.001) (R²=0.39).

Health status was not significant in any of the models (p > 0.05), possibly because health needs did not directly affect the service experience.

Table 2: Regression Analysis Results

4.2 Hypothesis Validation

Hypothesis 1: The Digital Divide Exacerbates the Unfairness of Smart Elderly Care Policies

The digital divide significantly worsens the unfairness of smart elderly care policies by limiting access to technology-based services. Findings from Tables 1 and 2 strongly support this hypothesis. Regional disparities and digital literacy emerged as key determinants of service usage, perceived fairness, and satisfaction (p<0.001). For instance, elderly individuals in underdeveloped regions like Meizhou and Heyuan had much lower service usage (M=1.8, SD=0.9) than those in developed areas such as Shenzhen and Guangzhou (M=2.8, SD=1.0, t=12.45, p<0.001). This reflects the impact of weaker infrastructure and limited access to smart technology. Regression analysis confirmed the negative effect of regional disparity (β=-0.42, p<0.001), while digital literacy positively influenced service usage (β=0.35, p<0.001). These findings echo existing literature that identifies digital exclusion as a driver of inequality in elderly care (Gao & Chen, 2023). Moreover, perceived fairness ratings were significantly lower in underdeveloped regions (M=2.0, SD=0.8) compared to developed ones (M=3.0, SD=0.9, t=14.78, p<0.001), further illustrating how the digital divide deepens policy implementation gaps.

Hypothesis 2: Smart Elderly Care Policies Have a Greater Negative Impact on Elderly Individuals with Low Digital Literacy

The second hypothesis is also strongly supported. Elderly individuals with low digital literacy (score ≤ 2) reported significantly lower service usage (M=1.7, SD=0.8), perceived fairness (M=2.1, SD=0.7), and satisfaction (M=2.3, SD=0.8) than their higher-literacy counterparts (usage: M=3.1, fairness: M=3.0, satisfaction: M=3.3, all p<0.001). This demonstrates that limited technical skills hinder effective participation in smart care systems. Regression results showed digital literacy significantly improved outcomes across all indicators (β=0.32–0.40, p<0.001). A one-point increase in digital literacy corresponded with a 0.40 rise in service satisfaction. These outcomes align with prior research showing that digital illiteracy leads to technological exclusion, thereby limiting the effectiveness of smart elderly care policies for disadvantaged groups (Lee et al., 2021). This highlights the urgent need for interventions such as digital training and support services for low-literacy seniors.

Hypothesis 3: Policy Optimization and Technology Popularization Can Narrow the Digital Divide and Enhance Fairness

The third hypothesis receives indirect support. While this study did not test specific interventions, the data suggest that policy and technological initiatives could help bridge the digital gap. The positive effects of digital literacy (β=0.35 for usage, β=0.40 for satisfaction) imply that technology education could significantly improve access and engagement. Meanwhile, the persistent negative influence of regional disparities (β=-0.35 to -0.42, p<0.001) indicates a need for targeted infrastructure investment. Prior research supports these findings, emphasizing that inclusive design, simplified interfaces, and rural broadband expansion are essential for achieving equitable smart elderly care (Wang & Zhang, 2023). Overall, the evidence points to the value of optimizing both policy frameworks and technological tools to ensure fair access for all elderly individuals.

4.3 Discussion

The results indicate that the digital divide is a core factor contributing to inequality in the implementation of smart elderly care policies, which is consistent with previous findings (Zhang, 2020). This divide manifests not only in infrastructure and device access but also in the digital capabilities of elderly individuals, particularly in less developed regions. The clear disadvantage observed in underdeveloped areas—where service usage (M=1.8) and perceived fairness (M=2.0) are significantly lower—mirrors the persistent urban-rural gap in digital development and care access (Xu et al., 2023). In particular, the low scores of the low digital literacy group (e.g., service usage M=1.7) underscore how essential digital competence is for engaging with smart care services (Sun, 2024).

These findings reflect the double-edged sword effect of smart elderly care policies. On the one hand, the integration of digital tools and platforms can greatly enhance service efficiency, personalization, and responsiveness. On the other hand, if the digital divide remains unaddressed, these technologies risk reinforcing existing social inequalities, disproportionately excluding those with limited digital access and literacy.

To bridge this gap, policies should prioritize accessible training programs tailored to seniors’ needs. For instance, community workshops could introduce basic app navigation before progressing to advanced tools like telemedicine, leveraging intergenerational support to enhance engagement (Chen et al., 2023). Such efforts could amplify service usage, as digital literacy strongly predicts adoption (β=0.35, p<0.001). From a psychological perspective, low digital literacy may exacerbate social isolation, particularly in rural settings with limited community resources (Tsai et al., 2022). Designing intuitive interfaces, such as voice-activated systems, aligns with TAM’s emphasis on perceived ease of use, fostering greater acceptance. Internationally, Singapore’s Smart Nation initiative, which integrates subsidized devices and digital literacy courses for seniors, offers a replicable model (Lim & Pang, 2021). Targeted investments in regions like Meizhou could mitigate regional disparities (β=-0.42, p<0.001). Looking ahead, AI-driven assistive technologies, designed for compatibility with seniors’ routines per IDT, promise to reduce barriers, provided affordability and simplicity are prioritized (Marston & Musselwhite, 2021).

From a theoretical perspective, the results offer empirical support for the integration of the Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT). According to TAM, perceived ease of use—represented here by digital literacy—was shown to have a significant impact on service usage (β=0.35). This suggests that elderly individuals with better digital skills are more likely to accept and adopt smart elderly care services. Simultaneously, IDT emphasizes the role of social system conditions in the spread of innovation. In this study, regional disparities—a key social system variable—had a strong negative effect on service usage (β=-0.42), indicating that uneven regional development hinders the widespread adoption of smart care technologies. Thus, this research confirms the complementarity of TAM and IDT in explaining both micro- and macro-level influences on technology use among the elderly.

Compared to previous studies, this research adds value by quantifying the multidimensional effects of the digital divide. The βvalue for regional disparity (-0.42) reveals that geographical inequality is the most powerful predictor of service usage gaps. This insight is crucial for policymakers seeking to promote digital inclusion. Additionally, the challenges faced by individuals with low digital literacy align with Lee’s (2021) analysis of “technological conservatism” which describes how fear, unfamiliarity, and mistrust toward digital devices reduce elderly engagement.

The study’s findings offer several practical implications. First, the strong positive influence of digital literacy (β=0.32–0.40) highlights the value of community-based digital training programs tailored to older adults. Second, the design of smart elderly care technologies should prioritize usability—simple interfaces, voice-assisted functions, and visual aids can significantly lower usage barriers. Lastly, the profound impact of regional disparities points to the urgent need for targeted public investments in rural broadband, smart infrastructure, and technical support services to ensure that smart elderly care policies are not only advanced but also equitable.

In sum, this study calls for a more inclusive and context-sensitive approach to smart elderly care—one that recognizes and actively addresses the structural digital divides that shape how older adults experience technology-enabled services.

5. Conclusion and Recommendations

5.1 Research Summary

This study explored the impact of the digital divide on the fairness of smart elderly care policies in China through quantitative analysis and tested the related hypotheses. Findings show that unequal access to technology plays a major role in creating unfairness in how smart elderly care policies are applied. Elderly individuals in underdeveloped regions scored significantly lower than those in developed regions in terms of service usage (M=1.8 vs. M=2.8) and perceived fairness (M=2.0 vs. M=3.0). The low digital literacy group (service usage M=1.7) also faced greater barriers, confirming Hypothesis 1 (the digital divide exacerbates policy unfairness) and Hypothesis 2 (the low digital literacy group is more negatively affected).

Regression analysis further revealed that regional disparities (β=-0.42) and digital literacy (β=0.35–0.40) are key factors influencing service usage and perceived fairness. Hypothesis 3 (policy optimization and technology popularization can narrow the digital divide) was indirectly supported, as the positive effect of digital literacy suggests that improving technological capabilities may enhance service adoption and fairness.

The study highlights the double-edged sword effect of smart elderly care policies: while technology improves service efficiency, the digital divide marginalizes certain groups and exacerbates inequality.

5.2 Suggestions for Policy and Practice

Based on the research findings, the following recommendations are proposed to narrow the digital divide and improve the fairness of smart elderly care services:

Policy Optimization: Governments should develop more targeted policies to address the needs of elderly individuals in underdeveloped regions and those with low digital literacy. For example, special subsidies could be provided to supply smart devices for low-income elderly individuals, and network infrastructure in remote areas (e.g., 5G coverage) could be improved to reduce inequalities caused by regional disparities (β=-0.42).

Technology Popularization: Develop senior-friendly smart devices with simplified interfaces and provide technology training programs. Research shows that improving digital literacy (β=0.35–0.40) significantly enhances service usage and satisfaction. It is recommended that communities regularly organize technology training sessions to teach elderly individuals how to use health monitoring devices and telemedicine services.

Resource Allocation and Social Collaboration: Governments should optimize resource allocation, prioritizing regions and groups severely affected by the digital divide. Public, private, and social sectors should collaborate to build an inclusive elderly care service system. For instance, companies could develop low-cost smart devices, and social organizations could provide technical support to jointly create an inclusive elderly care ecosystem.

Enhancing Policy Transparency: Open discussions about the digital divide and increase policy transparency to enhance public trust and support for smart elderly care policies.

5.3 Research Limitations and Future Directions

Although this study provides valuable findings, it has certain limitations. First, the sample was limited to four cities in Guangdong Province and did not cover the diversity of other regions in China. Future research could expand the sample scope to improve external validity. Second, this study adopted a cross-sectional design, which makes it difficult to capture the long-term dynamic changes in the adoption of smart elderly care services. Longitudinal studies are recommended in the future to track the long-term effects of policy optimization and technology popularization.

Additionally, further exploration of other potential variables (e.g., social support, cultural factors) that may influence the adoption of smart elderly care services could provide a more comprehensive understanding of the complexity of the digital divide.

The theoretical contribution of this study lies in validating the integrated model of TAM and IDT, while its practical contribution provides empirical evidence for policymakers to promote fairness and sustainability in smart elderly care services.

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